263 search results for "PCA"

Mythbusting – Dr. Copper

April 21, 2014
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Mythbusting – Dr. Copper

Image by Justin Reznick   “An economist is an expert who will know tomorrow why the things he predicted yesterday didn't happen today.” Laurence J. Peter (author and creator of the Peter Principle) If you were paying attention to financial sites last month, you probably noticed a number of articles on “Dr. Copper”. Here is

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Geomorph 3D Visualization

April 16, 2014
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Geomorph 3D Visualization

Dear geomorph users,version 2.0 of geomorph brings new developments in how shape deformations from 3D coordinate shape data can be viewed. We have implemented warping of 3D surface files (e.g., .ply files), which allows the user to visualize the shape deformations along Principal Component axes, Multivariate Regression slopes, Partial Least Squares axes and group differences, to name a few.The new function warpRefMesh() reads in a .ply...

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Visualizing principal components with R and Sochi Olympic Athletes

March 27, 2014
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Visualizing principal components with R and Sochi Olympic Athletes

Principal Components Analysis (PCA) is used as a dimensionality reduction method. Here we simply explain PCA step-by-step using data about Sochi Olympic Curlers. It is hard to visualize a high dimensional space. When I took linear algebra, the book and teachers spoke about it as if were easy to visualize a hyperspace, but...

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sjPlot 1.3 available #rstats #sjPlot

March 27, 2014
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sjPlot 1.3 available #rstats #sjPlot

I just submitted my package update (version 1.3) to CRAN. The download is already available (currently source, binaries follow). While the last two updates included new functions for table outputs (see here and here for details on these functions), the current update only provides small helper functions as new functions. The focus of this update

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Beautiful table outputs in R, part 2 #rstats #sjPlot

March 4, 2014
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Beautiful table outputs in R, part 2 #rstats #sjPlot

First of all, I’d like to thank my readers for the lots of feedback on my last post on beautiful outputs in R. I tried to consider all suggestions, updated the existing table-output-functions and added some new ones, which will be described in this post. The updated package is already available on CRAN. This posting

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Genetic data, large matrices and glmnet()

February 25, 2014
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Genetic data, large matrices and glmnet()

Recently talking to a colleague, had contact with a problem that I had never worked with before: modeling with genetic The post Genetic data, large matrices and glmnet() appeared first on Flavio Barros .

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Interactive exploration of a prior’s impact

February 21, 2014
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Interactive exploration of a prior’s impact

The probably most frequent criticism of Bayesian statistics sounds something like “It’s all subjective – with the ‘right’ prior, you can get any result you want.”. In order to approach this criticism it has been suggested to do a sensitivity analysis (or robustness analysis), that demonstrates how the choice of priors affects the conclusions drawn

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Regression with multiple predictors

February 18, 2014
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(This article was first published on Digithead's Lab Notebook, and kindly contributed to R-bloggers) Now that I'm ridiculously behind in the Stanford Online Statistical Learning class, I thought it would be fun to try to reproduce the figure on page 36 of the slides from chapter 3 or page 81 of the book. The result is a curvaceous surface...

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ggplot2: Cheatsheet for Visualizing Distributions

February 18, 2014
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ggplot2: Cheatsheet for Visualizing Distributions

In the third and last of the ggplot series, this post will go over interesting ways to visualize the distribution of your data.

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Tutorials- Statistical and Multivariate Analysis for Metabolomics

February 17, 2014
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Tutorials- Statistical and Multivariate Analysis for Metabolomics

I recently had the pleasure in participating in the 2014 WCMC Statistics for Metabolomics Short Course. The course was hosted by the NIH West Coast Metabolomics Center and focused on statistical and multivariate strategies for metabolomic data analysis. A variety of topics were covered using 8 hands on tutorials which focused on: data quality overview

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